{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 10.4 - PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.532282Z",
     "start_time": "2023-07-05T11:58:57.416764Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10.4 Lab 1: Principal Components Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.544518Z",
     "start_time": "2023-07-05T11:58:57.532833Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "   Unnamed: 0  Murder  Assault  UrbanPop  Rape\n0     Alabama    13.2      236        58  21.2\n1      Alaska    10.0      263        48  44.5\n2     Arizona     8.1      294        80  31.0\n3    Arkansas     8.8      190        50  19.5\n4  California     9.0      276        91  40.6",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Murder</th>\n      <th>Assault</th>\n      <th>UrbanPop</th>\n      <th>Rape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Alabama</td>\n      <td>13.2</td>\n      <td>236</td>\n      <td>58</td>\n      <td>21.2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Alaska</td>\n      <td>10.0</td>\n      <td>263</td>\n      <td>48</td>\n      <td>44.5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Arizona</td>\n      <td>8.1</td>\n      <td>294</td>\n      <td>80</td>\n      <td>31.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Arkansas</td>\n      <td>8.8</td>\n      <td>190</td>\n      <td>50</td>\n      <td>19.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>California</td>\n      <td>9.0</td>\n      <td>276</td>\n      <td>91</td>\n      <td>40.6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the data \n",
    "data = pd.read_csv(r'../data/USArrests.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data contains states of US\n",
    "- Name  - name of the states\n",
    "- Murder - number of arrests for murder per million pop. \n",
    "- Assault - number of arrests for assaults per million pop.\n",
    "- Urbanpop - percentage of population living in urban area\n",
    "- Rape - number of rape cases per million population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.551919Z",
     "start_time": "2023-07-05T11:58:57.548116Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0            Alabama\n",
      "1             Alaska\n",
      "2            Arizona\n",
      "3           Arkansas\n",
      "4         California\n",
      "5           Colorado\n",
      "6        Connecticut\n",
      "7           Delaware\n",
      "8            Florida\n",
      "9            Georgia\n",
      "10            Hawaii\n",
      "11             Idaho\n",
      "12          Illinois\n",
      "13           Indiana\n",
      "14              Iowa\n",
      "15            Kansas\n",
      "16          Kentucky\n",
      "17         Louisiana\n",
      "18             Maine\n",
      "19          Maryland\n",
      "20     Massachusetts\n",
      "21          Michigan\n",
      "22         Minnesota\n",
      "23       Mississippi\n",
      "24          Missouri\n",
      "25           Montana\n",
      "26          Nebraska\n",
      "27            Nevada\n",
      "28     New Hampshire\n",
      "29        New Jersey\n",
      "30        New Mexico\n",
      "31          New York\n",
      "32    North Carolina\n",
      "33      North Dakota\n",
      "34              Ohio\n",
      "35          Oklahoma\n",
      "36            Oregon\n",
      "37      Pennsylvania\n",
      "38      Rhode Island\n",
      "39    South Carolina\n",
      "40      South Dakota\n",
      "41         Tennessee\n",
      "42             Texas\n",
      "43              Utah\n",
      "44           Vermont\n",
      "45          Virginia\n",
      "46        Washington\n",
      "47     West Virginia\n",
      "48         Wisconsin\n",
      "49           Wyoming\n",
      "Name: Unnamed: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "states = data.iloc[:,0]\n",
    "#names of the states in alphabatical order\n",
    "print(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.579764Z",
     "start_time": "2023-07-05T11:58:57.553178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "['Murder', 'Assault', 'UrbanPop', 'Rape']"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.iloc[:,1:]\n",
    "list(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.580439Z",
     "start_time": "2023-07-05T11:58:57.556286Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         Murder     Assault   UrbanPop       Rape\ncount  50.00000   50.000000  50.000000  50.000000\nmean    7.78800  170.760000  65.540000  21.232000\nstd     4.35551   83.337661  14.474763   9.366385\nmin     0.80000   45.000000  32.000000   7.300000\n25%     4.07500  109.000000  54.500000  15.075000\n50%     7.25000  159.000000  66.000000  20.100000\n75%    11.25000  249.000000  77.750000  26.175000\nmax    17.40000  337.000000  91.000000  46.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Murder</th>\n      <th>Assault</th>\n      <th>UrbanPop</th>\n      <th>Rape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>50.00000</td>\n      <td>50.000000</td>\n      <td>50.000000</td>\n      <td>50.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>7.78800</td>\n      <td>170.760000</td>\n      <td>65.540000</td>\n      <td>21.232000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>4.35551</td>\n      <td>83.337661</td>\n      <td>14.474763</td>\n      <td>9.366385</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.80000</td>\n      <td>45.000000</td>\n      <td>32.000000</td>\n      <td>7.300000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>4.07500</td>\n      <td>109.000000</td>\n      <td>54.500000</td>\n      <td>15.075000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>7.25000</td>\n      <td>159.000000</td>\n      <td>66.000000</td>\n      <td>20.100000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>11.25000</td>\n      <td>249.000000</td>\n      <td>77.750000</td>\n      <td>26.175000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>17.40000</td>\n      <td>337.000000</td>\n      <td>91.000000</td>\n      <td>46.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can from the above table that they have irregular ranges, and assault has the highest value of mean and also std (also variance which is square of std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, if we appply PCA to this data, it will give the highest priority to assault, and to avoid this, we will be scaling the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.598644Z",
     "start_time": "2023-07-05T11:58:57.572273Z"
    }
   },
   "outputs": [],
   "source": [
    "# scaling the data \n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.656730Z",
     "start_time": "2023-07-05T11:58:57.579066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     Murder   Assault  UrbanPop      Rape\n0  1.255179  0.790787 -0.526195 -0.003451\n1  0.513019  1.118060 -1.224067  2.509424\n2  0.072361  1.493817  1.009122  1.053466\n3  0.234708  0.233212 -1.084492 -0.186794\n4  0.281093  1.275635  1.776781  2.088814",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Murder</th>\n      <th>Assault</th>\n      <th>UrbanPop</th>\n      <th>Rape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.255179</td>\n      <td>0.790787</td>\n      <td>-0.526195</td>\n      <td>-0.003451</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.513019</td>\n      <td>1.118060</td>\n      <td>-1.224067</td>\n      <td>2.509424</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.072361</td>\n      <td>1.493817</td>\n      <td>1.009122</td>\n      <td>1.053466</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.234708</td>\n      <td>0.233212</td>\n      <td>-1.084492</td>\n      <td>-0.186794</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.281093</td>\n      <td>1.275635</td>\n      <td>1.776781</td>\n      <td>2.088814</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled_df = pd.DataFrame(X_scaled,columns = X.columns)\n",
    "X_scaled_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.694599Z",
     "start_time": "2023-07-05T11:58:57.590507Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "             Murder       Assault      UrbanPop          Rape\ncount  5.000000e+01  5.000000e+01  5.000000e+01  5.000000e+01\nmean  -7.105427e-17  1.387779e-16 -4.396483e-16  8.593126e-16\nstd    1.010153e+00  1.010153e+00  1.010153e+00  1.010153e+00\nmin   -1.620693e+00 -1.524362e+00 -2.340661e+00 -1.502548e+00\n25%   -8.611383e-01 -7.486054e-01 -7.704502e-01 -6.640245e-01\n50%   -1.247758e-01 -1.425453e-01  3.210209e-02 -1.220847e-01\n75%    8.029251e-01  9.483628e-01  8.521012e-01  5.330962e-01\nmax    2.229265e+00  2.015028e+00  1.776781e+00  2.671197e+00",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Murder</th>\n      <th>Assault</th>\n      <th>UrbanPop</th>\n      <th>Rape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>5.000000e+01</td>\n      <td>5.000000e+01</td>\n      <td>5.000000e+01</td>\n      <td>5.000000e+01</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-7.105427e-17</td>\n      <td>1.387779e-16</td>\n      <td>-4.396483e-16</td>\n      <td>8.593126e-16</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.010153e+00</td>\n      <td>1.010153e+00</td>\n      <td>1.010153e+00</td>\n      <td>1.010153e+00</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-1.620693e+00</td>\n      <td>-1.524362e+00</td>\n      <td>-2.340661e+00</td>\n      <td>-1.502548e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-8.611383e-01</td>\n      <td>-7.486054e-01</td>\n      <td>-7.704502e-01</td>\n      <td>-6.640245e-01</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-1.247758e-01</td>\n      <td>-1.425453e-01</td>\n      <td>3.210209e-02</td>\n      <td>-1.220847e-01</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>8.029251e-01</td>\n      <td>9.483628e-01</td>\n      <td>8.521012e-01</td>\n      <td>5.330962e-01</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2.229265e+00</td>\n      <td>2.015028e+00</td>\n      <td>1.776781e+00</td>\n      <td>2.671197e+00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the mean is approximately 0, and std is approximately 1 for all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.694979Z",
     "start_time": "2023-07-05T11:58:57.598848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "PCA(n_components=4)",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=4)</pre></div></div></div></div></div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using pca to get principal components \n",
    "pca = PCA(n_components=4)\n",
    "pca.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.757298Z",
     "start_time": "2023-07-05T11:58:57.602877Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "              PC_1      PC_2      PC_3      PC_4\nMurder    0.535899  0.418181 -0.341233  0.649228\nAssault   0.583184  0.187986 -0.268148 -0.743407\nUrbanPop  0.278191 -0.872806 -0.378016  0.133878\nRape      0.543432 -0.167319  0.817778  0.089024",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PC_1</th>\n      <th>PC_2</th>\n      <th>PC_3</th>\n      <th>PC_4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Murder</th>\n      <td>0.535899</td>\n      <td>0.418181</td>\n      <td>-0.341233</td>\n      <td>0.649228</td>\n    </tr>\n    <tr>\n      <th>Assault</th>\n      <td>0.583184</td>\n      <td>0.187986</td>\n      <td>-0.268148</td>\n      <td>-0.743407</td>\n    </tr>\n    <tr>\n      <th>UrbanPop</th>\n      <td>0.278191</td>\n      <td>-0.872806</td>\n      <td>-0.378016</td>\n      <td>0.133878</td>\n    </tr>\n    <tr>\n      <th>Rape</th>\n      <td>0.543432</td>\n      <td>-0.167319</td>\n      <td>0.817778</td>\n      <td>0.089024</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading vectors\n",
    "pd.DataFrame(pca.components_,columns = X.columns,index = ['PC_1','PC_2','PC_3','PC_4']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above table we can see the loding vectors as combinations of each principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:57.757792Z",
     "start_time": "2023-07-05T11:58:57.610835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": "       PC_1      PC_2      PC_3      PC_4\n0  0.985566  1.133392 -0.444269  0.156267\n1  1.950138  1.073213  2.040003 -0.438583\n2  1.763164 -0.745957  0.054781 -0.834653\n3 -0.141420  1.119797  0.114574 -0.182811\n4  2.523980 -1.542934  0.598557 -0.341996",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PC_1</th>\n      <th>PC_2</th>\n      <th>PC_3</th>\n      <th>PC_4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.985566</td>\n      <td>1.133392</td>\n      <td>-0.444269</td>\n      <td>0.156267</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.950138</td>\n      <td>1.073213</td>\n      <td>2.040003</td>\n      <td>-0.438583</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.763164</td>\n      <td>-0.745957</td>\n      <td>0.054781</td>\n      <td>-0.834653</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.141420</td>\n      <td>1.119797</td>\n      <td>0.114574</td>\n      <td>-0.182811</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.523980</td>\n      <td>-1.542934</td>\n      <td>0.598557</td>\n      <td>-0.341996</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the points in principal components\n",
    "pca_components = pca.transform(X_scaled)\n",
    "print(pca_components.shape)\n",
    "df = pd.DataFrame(pca_components,columns = ['PC_1','PC_2','PC_3','PC_4'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:58.128975Z",
     "start_time": "2023-07-05T11:58:57.619430Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x1200 with 1 Axes>",
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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# biplot \n",
    "fig, ax = plt.subplots(figsize = (20,12))\n",
    "ax.grid(True)\n",
    "ax.scatter(df['PC_1'],df['PC_2'])\n",
    "\n",
    "names = list(data.iloc[:,0])\n",
    "\n",
    "for i, txt in enumerate(names):\n",
    "    ax.annotate(txt, (df['PC_1'][i], df['PC_2'][i]))\n",
    "    \n",
    "xvector = pca.components_[0] # see 'prcomp(my_data)$rotation' in R\n",
    "yvector = pca.components_[1]\n",
    "\n",
    "xs = pca.transform(X_scaled)[:,0] # see 'prcomp(my_data)$x' in R\n",
    "ys = pca.transform(X_scaled)[:,1]\n",
    "\n",
    "\n",
    "## visualize projections\n",
    "    \n",
    "## Note: scale values for arrows and text are a bit inelegant as of now,\n",
    "##       so feel free to play around with them\n",
    "\n",
    "for i in range(len(xvector)):\n",
    "# arrows project features (ie columns from csv) as vectors onto PC axes\n",
    "    plt.arrow(0, 0, xvector[i], yvector[i],\n",
    "              color='r', width=0.0005, head_width=0.0025)\n",
    "    plt.text(xvector[i]*1.2, yvector[i]*1.2,\n",
    "             list(X.columns.values)[i], color='r')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:58.129292Z",
     "start_time": "2023-07-05T11:58:58.006505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([2.53085875, 1.00996444, 0.36383998, 0.17696948])"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# varianc explained by each pc's\n",
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:58.129410Z",
     "start_time": "2023-07-05T11:58:58.010163Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.62006039, 0.24744129, 0.0891408 , 0.04335752])"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variance explained ratio's \n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the first pc explaines 2.53 units of variance, which is around 62% of the total variance, the second pc expains 34.7 % of the variance in the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:58.197050Z",
     "start_time": "2023-07-05T11:58:58.015048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: >"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(pca.explained_variance_ratio_,index = ['PC_1','PC_2','PC_3','PC_4']).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:58.635004Z",
     "start_time": "2023-07-05T11:58:58.136930Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: >"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(np.cumsum(pca.explained_variance_ratio_),index = ['PC_1','PC_2','PC_3','PC_4']).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:58:58.635233Z",
     "start_time": "2023-07-05T11:58:58.229926Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
